CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

Connor Jerzak | Adel Daoud

Paper Code

Abstract: The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imagery into the study of cause and effect. One set of functions enables image-based causal inference analyses. For example, one key function decomposes treatment effect heterogeneity by images using an interpretable Bayesian framework. This allows for determining which types of images or image sequences are most responsive to interventions. A second modeling function allows researchers to control for confounding using images. The package also allows investigators to produce embeddings that serve as vector summaries of the image or video content. Finally, infrastructural functions are also provided, such as tools for writing large-scale image and image sequence data as sequentialized byte strings for more rapid image analysis. causalimages therefore opens new capabilities for causal inference in R, letting researchers use informative imagery in substantive analyses in a fast and accessible manner.

References

Connor T. Jerzak, Adel Daoud. CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images. ArXiv Preprint, 2023.
@article{jerzak2023causalimages,
  title={CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images},
  author={Jerzak, Connor T. and Adel Daoud},
  journal={ArXiv Preprint},
  year={2023},
  volume={},
  number={},
  pages={},
  publisher={}
}

Related Work

Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Image-based Treatment Effect Heterogeneity. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR), 213: 531-552, 2023.
@article{jerzak2023image,
  title={Image-based Treatment Effect Heterogeneity},
  author={Jerzak, Connor T. and Fredrik Johansson and Adel Daoud},
  journal={Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR)},
  year={2023},
  volume={213},
  pages={531-552},
  publisher={}
}
[Overview][Data][Code]

Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities. ArXiv Preprint, 2023.
@article{jerzak2023integrating,
  title={Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities},
  author={Jerzak, Connor T. and Fredrik Johansson and Adel Daoud},
  journal={ArXiv Preprint},
  year={2023},
  volume={},
  pages={},
  publisher={}
}
[Overview][Data][Code]

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